System for tracking and identifying vehicles and pedestrians

ABSTRACT

A system and method for monitoring locating people and vehicles based upon information is collected by a wide array of sensors already included in modern motor vehicles. Also included is a system of locating people and vehicles by aggregating data collected by an array of vehicles.

This application claims the benefit of U.S. Provisional Application No.62/421,022 having a filing date of Nov. 11, 2016 which is incorporatedby reference as if fully set forth.

BACKGROUND

There are currently an estimated 260 million cars in the United Statesthat drive annually a total of 3.2 trillion miles. Each modern car hasupwards of 200 sensors. As a point of reference, the Sojourner Rover ofthe Mars Pathfinder mission had only 12 sensors, traveled a distance ofjust over 100 meters mapping the Martian surface, and generated 2.3billion bits of information including 16,500 pictures and made 8.5million measurements. Therefore, there is an unrealized potential toutilize the over 200 sensors on the 260 million cars to collect detailedinformation about our home planet.

BRIEF DESCRIPTION OF THE DRAWINGS

A more detailed understanding may be had from the following description,given by way of example in conjunction with the accompanying drawingswherein:

FIG. 1 is an example system that uses a network of vehicles to locatepeople and vehicles.

FIG. 2 is a communication diagram for a vehicle.

FIG. 3 is a block diagram of the vehicle computer.

FIG. 4 is a block diagram of the database server.

FIG. 5 is an illustration of the “Bubbles of Vision” of a vehicle.

FIG. 6 is an illustration of the interaction of the “Bubbles of Vision”of two vehicles.

FIG. 7 is a block diagram of a process for locating a vehicle.

FIG. 8 is a block diagram of a process for locating a person.

FIG. 9 is a block for a server to locate a person or a vehicle.

FIG. 10 is an illustration of people and vehicles in the “Bubble ofVision”.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

A wide array of sensors is required for the modern operation of a motorvehicle. These sensors are required for the vehicle to navigate, avoidcollisions with other cars, and adjust the operating parameters of thedrive systems. However, the data collected by these sensors is confinedto the vehicle, is ephemeral, and is only used locally in the vehicle.The present disclosure provides a system which utilizes the data alreadybeing collected by the motor vehicle to convert the motor vehicle into a“rolling laboratory” for locating pedestrians and vehicles. Further, thesystem aggregates the data collected from a plurality of vehicles so thelocations of vehicles and individuals can be monitored over an extendedperiod of time.

Advanced driver assistance systems (ADAS) automate and enhance thesafety system of a vehicle and provide a more pleasurable drivingexperience. Examples of ADAS systems currently available includeAdaptive Cruise Control, Lane Departure Warning Systems, Blind SpotDetectors, and Hill Decent Control. In order to implement these systems,a wide array of sensors is required.

In the United States, there are numerous emergency alerts thatdisseminate via commercial radio stations, Internet radio, satelliteradio, television stations, and cable TV by the Emergency Alert System.One of these alerts is an Amber alert. Amber alerts are issued for childabductions. In order to issue an Amber alert, law enforcement mustdetermine that four criteria are met: (1) law enforcement must confirmthat an abduction has taken place; (2) the child must be at risk ofserious injury or death; (3) there must be sufficient descriptiveinformation of child, captor, or captor's vehicle to issue an alert; and(4) the child must be under 18 years of age. Accordingly, when an Amberalert is issued, information is either known about (a) the child, (b)the abductor or (c) the abductor's vehicle.

The present scheme uses a network of cars equipped with an ADAS systemthat are constantly collecting data about the environment surroundingthe vehicle to locate the child, the abductor, and the abductor'svehicle. This collected information is then analyzed by a vehiclecomputer. The vehicle computer then determines if the child, theabductor, or the abductor's vehicle is contained within the collectedinformation. Then, based on the determination, the vehicle computer maytransmit data to a server and contact emergency service officials.

FIG. 1 is a diagram of an example system practicing the method forlocating the child, the abductor, or the abductor's vehicle. In thesystem, an array of vehicles 110A . . . 110B may be communicativelycoupled to a database server 400 and be connected to the Internet 100via a wireless channel 105. The wireless communication channels 105 maybe of the form of any wireless communication mechanism such as LTE, 3G,WiMax etc.

Each vehicle in the array of vehicles 110A . . . 110B may contain avehicle computer (VC) 300 that is communicatively coupled to a pluralityof sensors 150. The sensors 150 may include thermal imagers, LIDAR,radar, ultrasonic, and high definition (HD) cameras. In addition,sensors 150 may also include air quality, temperature, radiation,magnetic field, and pressure that are used to monitor various systems ofthe vehicle.

Both the array of vehicles 110A . . . 110B and the database server 400may communicate with emergency services providers 130 over the Internet.The emergency services providers 130 may include fire, police or medicalservices.

The communicative connections of the VC 300 are graphically shown inFIG. 2. The VC 300 is communicatively coupled to a user interface 230.The VC 300 may instruct the user interface 230 to display informationstored in the memory 310 or storage 320 of the VC 300. In addition, theVC 300 may instruct the user interface 230 to display alert messages.The user interface 230 may include a touch screen that enables the userto input information to the VC 300. The user interface 230 may be adiscrete device or integrated into an existing vehicle entertainment ornavigation system.

The VC 300 may also be able to communicate with the Internet 100 via awireless communication channel 105. A database server 400 is alsoconnected to the Internet 100 via communication channel 125. It shouldbe understood that the Internet 100 may represent any network connectionbetween respective components.

The VC 300 is also communicatively coupled to a real time communicationinterface 250. The real time communication interface 250 enables the VC300 to access the Internet 100 over wireless communication channel 105.This enables the VC 300 to store and retrieve information stored indatabase server 400 in real time. The real time communication interface250 may include one or more antennas, receiving circuits, andtransmitting circuits. The wireless communication channel 105 providesnear real time communication of the VC 300 to the database while thevehicle is in motion.

Additionally, the VC 300 may communicate with the Internet 100 throughshort range wireless interface 260 over wireless communication channel290 via an access point 270. Wireless channel 290 may be 802.11 (WiFi),802.15 (Bluetooth) or any similar technology. Access point 270 may beintegrated in the charging unit of an electric vehicle, located at a gasrefueling station, or be located in an owner's garage. The wirelesschannel 290 allows the VC 300 to quickly and cheaply transmit largeamounts of data when the vehicle is not in motion and real time datatransmission is not required.

When the VC 300 detects that the short range wireless interface 260 isconnected to the Internet 100, the VC 300 transmits the data stored instorage 320 to the database 1100 over short range wireless channel 290.The VC 300 may then delete the data stored in storage 320.

The VC 300 may also be communicatively linked to a geo locating system240. The geo locating system 240 is able to determine the location ofthe vehicle 110 based on a locating standard such as the GlobalPositioning System (GPS) or Galileo.

The VC 300 may also be communicatively linked to the plurality ofsensors 150. The plurality of sensors may include one or more thermalimager 210 and one or more high definition camera 220. The thermalimager 210 may include any form of thermographic cameras such as aForward Looking Infrared (FLIR) camera. The high definition cameras 220may include any form of digital imaging device that captures images inthe visible light spectrum.

FIG. 3 depicts a block diagram of the VC 300. The VC 300 includes anInput/Output interface 330. The Input/Output interface 330 mayfacilitate communication of data with the plurality of sensors 150, userinterface 230, geo locating system 240, real time communicationinterface 250 and short range wireless interface 260. The VC 300 alsoincludes a processor 330 that is communicatively linked to theInput/Output interface 330, the memory 310 and the storage 320. Thestorage 320 may be a hard disk drive, solid state drive or any similartechnology for the nonvolatile storage and retrieval of data.

FIG. 4 depicts the components of the database server 400. The databaseserver 400 may include a memory 410, a communication interface 430,storage 420, and a processor 440. The processor 440 is able to transmitand receive information from the Internet 100 via the communicationinterface 430. In addition, the processor 440 is able to store datareceived by the communication 430.

FIG. 5 depicts various “Bubbles of Vision” associated with the differentsensors 150. Each bubble graphically represents a range of acorresponding sensor. For example, certain sensors have a higherresolution and limited sensing distance 535 from the vehicle 110. Othersensors have a much longer sensing range but have lower resolution 515.Yet other sensors operate in a medium sensing distance and resolution525. Although only 3 discrete Bubbles are shown, a person of ordinaryskill would understand that any number of layers can be included.Further, the Bubbles are shown depicted as oval merely for convenienceand the sensors 150 may produce sensing ranges of any shape.

FIG. 6 depicts the interaction of the “Bubbles of Vision” associatedwith two different vehicles 610A and 610B. Each vehicle has anassociated inner Bubble of Vision 635A and 335B, outer Bubble of Vision615A and 615B, and intermediate Bubble of Vision 625A and 625B. As aresult of the overlapping Bubble of Vision, multiple views andprospective of an object can be measured. The multiple views andprospective of the same object may be used to further identify theobject or to calibrate the sensors on a particular vehicle relative toanother vehicle.

FIG. 7 shows a block diagram for a method of locating a vehicle. Aplurality of images is acquired 705 from the sensors 150. The pluralityof images are then analyzed 710 to determine if the plurality of imagescontain another vehicle. If the plurality of images are determined tonot contain another vehicle, no further processing is required 715 andthe plurality of images are saved to the storage 320. If the pluralityof images are determined to contain another vehicle, it is determined ifan active emergency alert 720 has been received from the emergencyservices provider 130 that identifies a vehicle. The active emergencyalert may be received over the real time communication channel 105 orthe short range communication channel 290. In addition, the activeemergency alert may be directly received by the vehicle or indirectlyreceived via database server 400. If there is not an active emergencyalert that identifies a vehicle, no further processing is required 715and the plurality of images are saved to the storage 320. If there is anactive emergency alert that specifies a particular vehicle, a coarseanalysis of the plurality of images is performed 725 to determine if thevehicle contained in the images matches the vehicle specified in theactive emergency alert.

Methods for performing a coarse analysis of a plurality of images toidentify a particular vehicle are known in the art and include U.S. Pat.No. 6,747,687 to Alves for “System for recognizing the same vehicle atdifferent times and places”, which is hereby incorporated herein byreference.

For example, the active emergency alert may specify a 2014 Silver ToyotaCamry. The coarse analysis may generate a match for a 2012 White ToyotaCamry vehicle or a 2015 Silver Honda Civic. If the coarse analysis doesnot match the vehicle specified in the active alert, no furtherprocessing is required 715 and the plurality of images are saved to thestorage 320. If the coarse analysis does match the vehicle specified inthe active alert, the plurality of images are transmitted 730 to thedatabase server 400 over the real time wireless channel 105 foradditional analysis. In addition to the plurality of images, thetransmitted data may include geo location data and data from any of thesensors 150. Additionally, the plurality of images may be directlytransmitted to the emergency services provider 130 over the real timecommunication channel 105.

FIG. 8 shows a block diagram for a method of locating a person. Aplurality of images is acquired 805 from the sensors 150. The pluralityof images are then analyzed 810 to determine if the plurality of imagescontain a person. The person may be a pedestrian, operating of anothervehicle, or a passenger in another vehicle.

Methods to determine if an image contains a person are known in the artand include those taught in “Bag of Soft Biometrics for PersonIdentification” (Dantcheva, Antitza, Carmelo Velardo, Angela Da Angelo,and Jean-Luc Dugelay. “Bag of Soft Biometrics for PersonIdentification.” Multimedia Tools and Applications 51.2 (2010): 739-77.)and “Full-body Person Recognition System” (Nakajima, Chikahito,Massimiliano Pontil, Bernd Heisele, and Tomaso Poggio. “Full-body PersonRecognition System.” Pattern Recognition 36.9 (2003): 1997-2006), whichare hereby incorporated herein by reference.

If the plurality of images are determined to not contain a person, nofurther processing is required 815, and the plurality of images aresaved to the storage 320. If the plurality of images are determined tocontain a person, it is determined if an active emergency alert 820 hasbeen received from the emergency services provider 130 that identifies aperson. The active emergency alert may be received over the real timecommunication channel 105 or the short range communication channel 290.In addition, the active emergency alert may be directly received by thevehicle or indirectly received via database server 400. If there is notan active emergency alert that identifies a person, no furtherprocessing is required 815, and the plurality of images are saved to thestorage 320. If there is an active emergency alert that specifies aperson, a course analysis of the plurality of images is performed 825 todetermine if the person contained in the images matches the personspecified in the active emergency alert.

For example, the active emergency alert may specify a Michael Smith, a25 year old white male who is 6 feet tall and weighing 280 pounds. Thecoarse analysis may generate a match for any white male that is between5 feet 10 inches and 6 feet 2 inches. If the coarse analysis does notmatch the vehicle specified in the active alert, no further processingis required 815 and the plurality of images are saved to the storage320. If the coarse analysis does match the vehicle specified in theactive alert, the plurality of images are transmitted 830 to thedatabase server 400 over the real time wireless channel 105 foradditional analysis. In addition to the plurality of images, thetransmitted data may include geo location data and data from any of thesensors 150. Additionally, the plurality of images may be directlytransmitted to the emergency services provider 130 over the real timecommunication channel 105.

FIG. 9 shows a block diagram for a method of locating a vehicleimplemented in the database server 400. Data is received 905 from theplurality of vehicles 110 a . . . 110 n over the real time communicationinterface 105 and the short range communication interface 290. The datamay be received via communication interface 430. In addition to theplurality of images, the received data may include geolocation data anddata from any of the sensors 150. The received data is then compared todetermine if the data matches an active alert 910. If the data does notmatch an active alert, the received data is aggregated 915 based on thetime in which the data is recorded by the array of vehicles 110 a . . .110 n and the geolocation information. The aggregated data is thencompared to determine if the aggregated data matches any active alert920. For example, an individual vehicle may not be able to positivelyidentify a particular person or a particular vehicle. However, once datais collected from multiple vehicles, an identification may be possible.

Methods for identifying a person based on aggregated data are known inthe art and include such methods as taught in “Viewpoint Invariant HumanRe-Identification in Camera Networks Using Pose Priors andSubject-Discriminative Features.” (Wu, Ziyan, Yang Li, and Richard J.Radke. “Viewpoint Invariant Human Re-Identification in Camera NetworksUsing Pose Priors and Subject-Discriminative Features.” IEEETransactions on Pattern Analysis and Machine Intelligence 37.5 (2015):1095-108.) and “Person Tracking and Reidentification: IntroducingPanoramic Appearance Map (PAM) for Feature Representation” (Gandhi,Tarak, and Mohan Manubhai Trivedi. “Person Tracking andReidentification: Introducing Panoramic Appearance Map (PAM) for FeatureRepresentation.” Machine Vision and Applications 18.3-4 (2007):207-220.), which are hereby incorporated herein by reference.

Methods for identifying a vehicle based on aggregated data are known inthe art and include such methods as taught in “Vehicle Identificationbetween Non-overlapping Cameras without Direct Feature Matching” (Shan,Ying, H.s. Sawhney, and R. Kumar. “Vehicle Identification betweenNon-overlapping Cameras without Direct Feature Matching.” Tenth IEEEInternational Conference on Computer Vision (ICCV'05) Volume 1 (2005):n. pag) and “Intelligent Multi-camera Video Surveillance: A Review.”(Wang, Xiaogang. “Intelligent Multi-camera Video Surveillance: AReview.” Pattern Recognition Letters 34.1 (2013): 3-19), which arehereby incorporated herein by reference.

If the aggregated data matches an active emergency alert, the matchingaggregated data is transmitted 925 to the emergency services provider130. The data may be transmitted using communication interface 420. Ifthe aggregated data does not match an active alert, the aggregated datamay be stored 945 in storage 420.

If the received data matches an active alert 910, an additional analysisof the received data may be performed 930. For instance, if theemergency services alert is for a person, the additional analysis 930may include performing facial recognition.

Methods of performing facial recognition are known in the art andinclude such methods as taught in “Bayesian face recognition”(Moghaddam, Baback, Tony Jebara, and Alex Pentland. “Bayesian facerecognition.” Pattern Recognition 33.11 (2000): 1771-1782.), which ishereby incorporated herein by reference.

Similarly, if the alert specifies a vehicle, the additional analysis 930may include license plate recognition or identify additionalcharacteristics of the vehicle for such attributes as dents.

Methods of performing vehicle identification are known in the art andinclude the methods taught in “Automatic vehicle identification by platerecognition” (Ozbay, Serkan, and Ergun Ercelebi. “Automatic vehicleidentification by plate recognition.” World Academy of Science,Engineering and Technology 9.41 (2005): 222-225.), and “Learning-basedspatio-temporal vehicle tracking and indexing for transportationmultimedia database systems” (Chen, Shu-Ching, et al. “Learning-basedspatio-temporal vehicle tracking and indexing for transportationmultimedia database systems.” IEEE Transactions on IntelligentTransportation Systems 4.3 (2003): 154-167.), which are herebyincorporated herein by reference.

Based on the additional analysis, it is determined 935 if the receiveddata still matches the active emergency alert. If the additionalanalysis indicates that the received data no longer matches the activealert, the received data may be stored 945 in storage 420. If thereceived data still matches an active emergency alert, the matchingaggregated data is transmitted 945 to the emergency services provider130. The data may be transmitted using communication interface 420.

FIG. 10 is an illustration of a vehicle practicing an embodiment of thepresent disclosure. Person 1030A is located within Bubble of Vision 1015of vehicle 1010. Since person 1030A is with the Bubble of Vision 1015,the sensors 150 will be able to gather data about person 1030A. The dataacquired by the sensors 150 may be used to determine if person 1030Amatches the pedestrian specified in an active emergency alert.Pedestrian 1030B is located outside the Bubble of Vision 1015, andtherefore the sensors 150 would be unable to gather data on person1030B, and the system would be unable to determine if person 1030Bmatched the specified person. Similarly, vehicles 1020A and 1020B arelocated within Bubble of Vision 1015. As a result, the sensors 150 wouldbe able to collect data on 1020A and 1020B. Then once the data wascollected, the system would be able to determine if 1020A and 1020Bmatch the vehicle specified in an active emergency alert.

Although features and elements are described above in particularcombinations, one of ordinary skill in the art will appreciate that eachfeature or element can be used alone or in any combination with theother features and elements. In addition, any of the steps describedabove may be automatically performed by either the VC 300 or databaseserver 400.

Furthermore, the methods described herein may be implemented in acomputer program, software, or firmware incorporated in acomputer-readable medium for execution by a computer or processor.Examples of computer-readable media include electronic signals(transmitted over wired or wireless connections) and non-transitorycomputer-readable storage media. Examples of non-transitorycomputer-readable storage media include, but are not limited to, a readonly memory (ROM), a random access memory (RAM), a register, cachememory, semiconductor memory devices, magnetic media, such as internalhard disks and

removable disks, magneto-optical media, and optical media such as CD-ROMdisks, and digital versatile disks (DVDs).

1. An apparatus for locating objects specified in an emergency alert,the apparatus comprising: one or more imagers, each imager having arespective bubble of vision, a real time communication interface, ashort range communication interface, a geo locating system to determinea location of the vehicle at a selected point in time; and a vehiclecomputer communicatively coupled to the one or more imagers, the geolocating system, the real time communication interface, and the shortrange communication interface; wherein the vehicle computer: acquires aplurality of images from the one or more imagers, determines if theplurality of images contain another vehicle, determines if an activealert has been received via the real time communication interface, andwhen the plurality of images contain another vehicle and an active alerthas been received via the real time communication interface, selectivelytransmits via the real time interface the plurality of images to adatabase server based on the vehicle analysis.
 2. The apparatus of claim1, wherein first and second imagers have first and second bubbles ofvision, wherein the first and second bubbles of vision overlap and havedifferent ranges to provide multiple views and perspective of an imagedobject, and wherein the vehicle computer further: selectively transmitsvia the short range communication interface the plurality of images tothe database server on a condition that the active alert has not beenreceived.
 3. The apparatus of claim 1, wherein the vehicle computerfurther: determines if the plurality of images contain an image of oneor more persons, performs a person analysis of the plurality of imageson a condition that the plurality of images contain the one or morepersons and the active alert specifies a particular person, andselectively transmits via the real time interface the plurality ofimages to the database server based on the person analysis.
 4. Theapparatus of claim 1, wherein the database server: receives theplurality of images via the real time communication interface, performsan additional analysis on the plurality of images on a condition thatthe plurality of images contain a person and the active alert specifiesa particular person, and selectively transmits the plurality of imagesto an emergency services provider based on the additional analysis,wherein the additional analysis includes facial recognition.
 5. Theapparatus of claim 2, wherein the database server: receives theplurality of images via the short range communication interface,aggregates the plurality of images based on temporal and geo locationinformation associated with the plurality of images to form aggregateddata, performs an additional analysis on the aggregated data, andselectively transmits the plurality of images to an emergency servicesprovider based on the additional analysis.
 6. The apparatus of claim 1,wherein the vehicle computer transmits the plurality of images with geolocation data providing a current location of the vehicle, wherein theadditional analysis comprises license plate recognition, and wherein thevehicle computer further: performs a vehicle analysis of the pluralityof images on a condition that the plurality of images contain anothervehicle and the active alert specifies a particular vehicle, andselectively displays an alert on a display communicatively coupled tothe vehicle computer based on the vehicle analysis.
 7. A method forlocating objects specified in an emergency alert, the method comprising:acquiring, by a vehicle, a plurality of images from one or more imagers,each imager having a respective bubble of vision determining, by thevehicle, if the plurality of images contain another vehicle,determining, by the vehicle, if an active alert has been received via areal time communication interface of the vehicle, and selectivelydisplaying, by the vehicle computer, an alert on a displaycommunicatively coupled to the vehicle computer based on the vehicleanalysis; and selectively transmitting, by the vehicle, via the realtime interface, the plurality of images to a database server based onthe vehicle analysis.
 8. The method of claim 7, wherein first and secondimagers have first and second bubbles of vision, wherein the first andsecond bubbles of vision overlap and have different ranges to providemultiple views and perspective of an imaged object, and furthercomprising: selectively transmitting, by the vehicle, via a short rangecommunication interface of the vehicle, the plurality of images to thedatabase server on a condition that the active alert has not beenreceived.
 9. The method of claim 7, further comprising: determining, bythe vehicle, if the plurality of images contain an image of one or morepersons, performing, by the vehicle, a person analysis of the pluralityof images on a condition that the plurality of images contain the one ormore persons and the active alert specifies a particular person, andselectively transmits, by the vehicle, via the real time interface, theplurality of images to the database server based on the person analysis.10. The method of claim 7, further comprising: receiving, by thedatabase server, the plurality of images via the real time communicationinterface, performing, by the database server, an additional analysis onthe plurality of images on a condition that the plurality of imagescontain a person and the active alert specifies a particular person, andselectively transmitting, by the database server, the plurality ofimages to an emergency services provider based on the additionalanalysis, wherein the additional analysis includes facial recognition.11. The method of claim 8, further comprising: receiving, by thedatabase server, the plurality of images via the short rangecommunication interface, aggregating, by the database server, theplurality of images based on temporal and geo location informationassociated with the plurality of images to form aggregated data,performing, by the database server, an additional analysis on theaggregated data, and selectively transmitting, by the database server,the plurality of images to an emergency services provider based on theadditional analysis.
 12. The method of claim 7, wherein the vehiclecomputer transmits the plurality of images with geo location dataproviding a current location of the vehicle, wherein the additionalanalysis comprises license plate recognition, and further comprising:performing, by the vehicle, a vehicle analysis of the plurality of theimages on a condition that the plurality of images contain anothervehicle and the active alert specifies a particular vehicle, andselectively displaying, by the vehicle computer, an alert on a displaycommunicatively coupled to the vehicle computer based on the vehicleanalysis.
 13. A system for locating objects specified in an emergencyalert, the system comprising: a database server communicatively coupledto the plurality of vehicles, wherein the database server includes: acommunication interface, a memory, storage, and a processorcommunicatively coupled to the memory, the storage and the communicationinterface; wherein the processor of the database server: receives, viathe communication interface, a plurality of images from a plurality ofvehicles, wherein the plurality of images include images acquired by oneor more imagers of each of the vehicles, aggregates the plurality ofimages based on geo location information and temporal informationprovided by the plurality of vehicles to form aggregated data, analyzesthe aggregated data to determine if the aggregated data matches anobject identified in an active alert received from an emergency servicesprovider to generate an analysis result, and selectively alerts theemergency service provider based on the analysis result.
 14. The systemof claim 13, wherein each of the plurality of vehicles: acquires theplurality of images from the one or more imagers, determines if theplurality of images contain another vehicle, determines if the activealert has been received via the real time communication interface,performs a vehicle analysis of the plurality of images on a conditionthat the plurality of images contain another vehicle and the activealert specifies a particular vehicle, and selectively transmits via thereal time interface the plurality of images to the database server basedon the vehicle analysis.
 15. The system of claim 13, wherein each of theplurality of vehicles further: selectively transmit via the short rangecommunication interface the plurality of images to the database serveron a condition that the active alert has not been received.
 16. Thesystem of claim 13, wherein the vehicle computer further: determine ifthe plurality of images contain an image of one or more persons, performa person analysis of the plurality of images on a condition that theplurality of images contain the one or more persons and the active alertspecifies a particular person, and selectively transmit via the realtime interface the plurality of images to the database server based onthe person analysis.
 17. The system of claim 15, wherein the databaseserver further: performs an additional analysis on the plurality ofimages on a condition that the plurality of images contain a person andthe active alert specifies a particular person, and selectivelytransmits the plurality of images to the emergency services providerbased on the additional analysis.
 18. The system of claim 17, whereinthe additional analysis includes facial recognition.
 19. The system ofclaim 13, wherein the plurality of vehicles comprise first and secondvehicles having imagers with first and second bubbles of vision,respectively, wherein the object is simultaneously within each of thefirst and second bubbles of vision, and wherein first and second imagesprovided by the imagers of the first and second vehicles, respectively,comprise different views of the object.
 20. The system of claim 14,wherein the vehicle analysis comprises license plate recognition.